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Bond D, Suzuki FA, Scalice RK (2020) Sheet metal joining process selector. J Braz Soc Mech Sci 42:. https://doi.org/10.1007/s40430-020-02310-9
Hultman H, Cedergren S, Söderberg R, Wärmefjord K (2020) Identification of variation sources for high precision fabrication in a digital twin context. In: ASME Int Mech Eng Congress Expo, Proceedings (IMECE). https://doi.org/10.1115/IMECE2020-23358
Kumar T, Kiran D V., Arora N (2021) Sheet metal joining and distortion measurement of aluminium alloy and steel in cold wire GTAW process. In: Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.12.038
Ceglarek D, Colledani M, Váncza J et al (2015) Rapid deployment of remote laser welding processes in automotive assembly systems. CIRP Ann 64:389–394. https://doi.org/10.1016/j.cirp.2015.04.119
Charles Liu S, Jack Hu S (1997) Variation simulation for deformable sheet metal assemblies using finite element methods. J Manuf Sci Eng, Transactions of the ASME 119. https://doi.org/10.1115/1.2831115
Tong X, Yu J, Zhang H et al (2023) Compliant assembly variation analysis of composite structures using the Monte Carlo method with consideration of stress-stiffening effects. Arch Appl Mech. https://doi.org/10.1007/s00419-023-02479-0
Liu X, An L, Wang Z, et al (2019) Assembly variation analysis of aircraft panels under part-to-part locating scheme. Int J Aerosp Eng https://doi.org/10.1155/2019/9563596
Xu C, Luo C, Zhou Y, Zhang G (2020) Variation simulation of multi-station flexible assembly based on finite element method. In: IEEE Int. Conf. Ind. Mechatron. Autom., ICMA. https://doi.org/10.1109/ICMA49215.2020.9233551
Thiruppathi R, Selvam G, Kannan MG, et al (2021) Optimization of body-in-white weld parameters for DP590 and EDD material combination. In: SAE Technical Paper. https://doi.org/10.4271/2021-28-0215
Vavilala VS (2020) Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network. Eur J Phys 41. https://doi.org/10.1088/1361-6404/ab7027
Xing YF (2017) Fixture layout design of sheet metal parts based on global optimization algorithms. J Manuf Sci Eng Transactions of the ASME 139. https://doi.org/10.1115/1.4037106
Rezaei Aderiani A, Wärmefjord K, Söderberg R, et al (2020) Optimal design of fixture layouts for compliant sheet metal assemblies. Int J Adv Manuf Technol 110. https://doi.org/10.1007/s00170-020-05954-y
Rezaei Aderiani A, Wärmefjord K, Söderberg R (2021) Evaluating different strategies to achieve the highest geometric quality in self-adjusting smart assembly lines. Robot Comput Integr Manuf 71:102164. https://doi.org/10.1016/j.rcim.2021.102164
Sinha S, Glorieux E, Franciosa P, Ceglarek D (2019) 3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds. In: Stella E (ed) Multimodal sensing: technologies and applications. SPIE, pp 89 – 101. https://doi.org/10.1117/12.2526062
Franciosa P, Palit A, Gerbino S, Ceglarek D (2019) A novel hybrid shell element formulation (QUAD+ and TRIA+): a benchmarking and comparative study. Finite Elem. Anal. Des. 166. https://doi.org/10.1016/j.finel.2019.103319
Yan S, Zhou Z, Dinavahi V (2018) Large-scale nonlinear device-level power electronic circuit simulation on massively parallel graphics processing architectures. IEEE Trans Power Electron 33:4660–4678. https://doi.org/10.1109/TPEL.2017.2725239
Khatouri H, Benamara T, Breitkopf P, Demange J (2022) Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey. Adv Model Simul Eng Sci 9:1. https://doi.org/10.1186/s40323-022-00214-y
Li B, Shui BW, Lau KJ (2002) Fixture configuration design for sheet metal assembly with laser welding: a case study. Int J Adv Manuf Technol 19:501–509. https://doi.org/10.1007/s001700200053
Gerbino S, Franciosa P, Patalano S (2015) Parametric variational analysis of compliant sheet metal assemblies with shell elements. In: Procedia CIRP. https://doi.org/10.1016/j.procir.2015.06.077
Franciosa P, Gerbino S, Ceglarek D (2016) Fixture capability optimisation for early-stage design of assembly system with compliant parts using nested polynomial chaos expansion. In: Procedia CIRP. https://doi.org/10.1016/j.procir.2015.12.101
Georgaka S, Stabile G, Star K, et al (2020) A hybrid reduced order method for modelling turbulent heat transfer problems. Comput Fluids 208. https://doi.org/10.1016/j.compfluid.2020.104615
Pfaller MR, Cruz Varona M, Lang J, et al (2020) Using parametric model order reduction for inverse analysis of large nonlinear cardiac simulations. Int J Numer Method Biomed Eng 36. https://doi.org/10.1002/cnm.3320
Zhang L, Zhang Y, van Keulen F (2023) Topology optimization of geometrically nonlinear structures using reduced-order modeling. Comput Methods Appl Mech Eng 416:116371. https://doi.org/10.1016/j.cma.2023.116371
Lall S, Marsden JE, Glavaški S (2002) A subspace approach to balanced truncation for model reduction of nonlinear control systems. Intl J Robust Nonlinear Control 12. https://doi.org/10.1002/rnc.657
Russo MB, Greco A, Gerbino S, Franciosa P (2023) Towards real-time physics-based variation simulation of assembly systems with compliant sheet-metal parts based on reduced-order models. In: Lecture Notes in Mechanical Engineering. https://doi.org/10.1007/978-3-031-15928-2_48
Chatterjee A (2000) An introduction to the proper orthogonal decomposition. Curr Sci 78. https://www.jstor.org/stable/24103957
Buhmann MD, Levesley J (2004) Radial basis functions: theory and implementations. Math Comput 73. https://doi.org/10.1017/CBO9780511543241
Nguyen MN, Kim HG (2022) An efficient PODI method for real-time simulation of indenter contact problems using RBF interpolation and contact domain decomposition. Comput Methods Appl Mech Eng 388. https://doi.org/10.1016/j.cma.2021.114215
Samuel JS, Muggeridge AH (2022) Fast modelling of gas reservoir performance with proper orthogonal decomposition based autoencoder and radial basis function non-intrusive reduced order models. J Pet Sci Eng 211:. https://doi.org/10.1016/j.petrol.2021.110011
Sun X, Pan X, Choi J il (2021) Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion. J Comput Appl Math 390. https://doi.org/10.1016/j.cam.2020.113372
Li T, Pan T, Zhou X, et al (2024) Non-intrusive reduced-order modeling based on parametrized proper orthogonal decomposition. Energies (Basel) 17. https://doi.org/10.3390/en17010146
Kang H, Tian Z, Chen G et al (2022) Investigation on the nonintrusive multi-fidelity reduced-order modeling for PWR rod bundles. Nucl Eng Technol 54:1825–1834. https://doi.org/10.1016/j.net.2021.10.036
Yu J, Yan C, Guo M (2019) Non-intrusive reduced-order modeling for fluid problems: a brief review. Proc Inst Mech Eng G J Aerosp Eng 233:5896–5912. https://doi.org/10.1177/0954410019890721
Shah A, Rimoli JJ (2022) Smart parts: data-driven model order reduction for nonlinear mechanical assemblies. Finite Elem Anal Des 200:103682. https://doi.org/10.1016/j.finel.2021.103682
Gao H, Wang J-X, Zahr MJ (2020) Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning. Physica D 412:132614. https://doi.org/10.1016/j.physd.2020.132614
Fang Z-Y, Xiao Z-W, Tsai C-W (2020) An effective multi-swarm algorithm for optimizing hyperparameters of DNN. In: Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications. ACM, New York, NY, USA, pp 1–6. https://doi.org/10.1145/3440943.3444722
Li W, Bazant MZ, Zhu J (2021) A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches. Comput Methods Appl Mech Eng 383:113933. https://doi.org/10.1016/j.cma.2021.113933
Fuhg JN, Fau A, Nackenhorst U (2021) State-of-the-art and comparative review of adaptive sampling methods for Kriging. Arch Comput Methods Eng 28. https://doi.org/10.1007/s11831-020-09474-6
Guénot M, Lepot I, Sainvitu C, et al (2013) Adaptive sampling strategies for non-intrusive POD-based surrogates. Eng Comput (Swansea, Wales) 30. https://doi.org/10.1108/02644401311329352
Wang J, Du X, Martins JRRA (2021) Novel adaptive sampling algorithm for POD-based non-intrusive reduced order model. In: AIAA Aviation and aeronautics forum and exposition, AIAA AVIATION Forum 2021. https://doi.org/10.2514/6.2021-3051
Saka Y, Gunzburger M, Burkardt J (2007) Latinized, improved LHS, and CVT point sets in hypercubes. Int J Numer Anal Model 4
Rippa S (1999) An algorithm for selecting a good value for the parameter c in radial basis function interpolation. Adv Comput Math 11. https://doi.org/10.1023/a:1018975909870
Franciosa et al. (2016) VRM simulation toolkit. Available on line: http://www2.warwick.ac.uk/fac/sci/wmg/research/manufacturing/downloads/
Babu PD, Gouthaman P, Marimuthu P (2019) Effect of heat sink and cooling mediums on ferrite austenite ratio and distortion in laser welding of duplex stainless steel 2205. Chin J Mech Eng-En 32. https://doi.org/10.1186/s10033-019-0363-5